1. Working in different contexts: RStudio Projects
2. Dynamic document generation: RMarkdown
3. Version control: Git + GitHub
4. Package management: renv
5. Containerization: Docker
6. Where should I start?
7. Start collaborating

• Peikert, A., & Brandmaier, A. M. (2020). A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker. https://doi.org/10.31234/osf.io/8xzqy

• What it does:
  • Allows to work in multiple different contexts (projects), e.g. one for each experiment
  • Each project is own working directory, workspace, history, and source documents
  • Each project is associated with a folder on your computer (= working directory)
• Why it helps:
  • Have a separate, shareable working environment for each experiment
  • Keep all the files associated with a project together — data, scripts, results, figures
  • Work on multiple projects at once, each associated with its packages (and package versions), loaded data, etc.
  • Use only relative paths
  • Necessary basis for version control

• In RStudio: File > New Project > …

• If necessary

• What it does:
  • Creates dynamic documents with embedded chunks of code (R, python, Julia, stan, …), computed results , written text etc. (= LaTex)
  • Markdown-files can be exported to documents (docx, rtf), presentations, pdfs, websites (html), … e.g using the knitr (Xie, 2015, 2020) and tinytex (Xie, 2015, 2020; for pdfs)
  • R code is dynamically rendered, and can be given in separate chunks (’’‘{r}’’‘) or inline (’ r … ’)
• Why it helps:
  • Simple language (\(\neq\) LaTex)
  • Integrates directly with statistical software (R Studio)
  • Saves code and output in one file
  • Reduces copy&paste errors: reported results consistent with actual results

• Installation: install.packages("rmarkdown")

• Installation: install.packages("rmarkdown")
• Open a markdown file: File > New File > R Markdown

• Installation: install.packages("rmarkdown")
• Open a markdown file: File > New File > R Markdown

• If necessary

• What it does:
  • …
  • …
• Why it helps:
  • …
  • …

• …

• If necessary

• What it does:
  • Creates a project-specific library of packages in the project folder
  • Overwrites install.packages() to install packages in this local library
  • Keeps track of package versions in the renv.lock file

• Why it helps:
  • Keeps package versions untouched by other projects
  • Allows you to revert to the previous state when an update has broken your analysis
  • Makes it easier to share package versions with your collaborators (e.g., via GitHub)

• Install renv just like any other R package via install.packages(renv)
• Initialize your project library via renv::init()
• Instead, you can also select “Use renv with this project” during project creation
• After successfully installing or updating packages, use renv::snapshot()
• If you want to revert to previous state (e.g., if an update caused problems), use renv::restore()

• Initialize renv for your sleepstudy project using renv::init()
• From the Files pane in RStudio, take a look at the renv.lock file
• Install a new package:

install.packages("cowsay")

• Acutally use the package in one of your scripts:

cowsay::say("Hello world", "cow")

• Write this change to the lockfile using renv::snapshot()
• Commit and push your changes to GitHub

Restoring someone else’s package versions:

1. Clone or pull the repository from GitHub
2. Open the the RStudio project (e.g. via the projectname.Rproj file)
3. Use renv::restore() to install the package versions from the renv.lock file

• There might be some (inconsequential) warnings when switching between Mac and Windows
• At least on Windows, you need to have Rtools installed when installing packages that are not on CRAN (https://cran.r-project.org/bin/windows/Rtools/)
• Installing and loading packages may take a while, especially if your project lives on a network drive
  (such as N:/)

• What it does:
  • Creates a small, linux-based virtual machine on your computer
  • Makes it possible to run your scripts (or render your .Rmd files) on this virtual system
  • The recipe to build this system is stored in a Dockerfile that can be shared via GitHub

• Why it helps:
  • Ensures long-term reproducibility regardless
  • Prevents differences between operating systems, base R versions, languages etc.

• Install Docker from https://docs.docker.com/installation/
• From the terminal, start a container and mount your local project folder:

docker run -d  -e PASSWORD=1234 -p 8787:8787 -v /path/to/your/project:/home/rstudio/ rocker/rstudio

• You can then access RStudio (running in the container) by opening http://localhost:8787 in your web browser (username: rstudio, password: 1234)
• You can also build your own container by:
  • Choosing a base image from https://hub.docker.com/u/rocker (e.g., one including the tidyverse or LaTeX)
  • Creating a Dockerfile in your project directy, specyfing additional steps to execute when building the container, e.g., install.packages("renv"); renv::restore()

• For detailled instructions, see Peikert & Brandmaier (2020)

• Some additional tools based on Docker:
  • With binder (https://mybinder.org) and Code Ocean (https://codeocean.com), you can run your analysis in the cloud; they will even create the Dockerfile for you if you don’t have your own one
  • Singularity (https://sylabs.io) is a fully compatible, open source clone of Docker which you can on systems where you don’t have root access (e.g., on high performance clusters)

• If necessary

• Suggested order of steps, minimal and maximal version

• …